OpenRaiser/Intern-Atlas huggingface dataset:
ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

Machine learning pipeline potentially consists of several stages of operations like data preprocessing, feature engineering and machine learning model training. Each operation has a set of hyper-parameters, which can become irrelevant for the pipeline when the operation is not selected. This gives rise to a hierarchical conditional hyper-parameter space. To optimize this mixed continuous and discrete conditional hierarchical hyper-parameter space, we propose an efficient pipeline search and configuration algorithm which combines the power of Reinforcement Learning and Bayesian Optimization. Empirical results show that our method performs favorably compared to state of the art methods like Auto-sklearn , TPOT, Tree Parzen Window, and Random Search.
Trusted CI Experiences in Cybersecurity and Service to Open Science
Venue: arXiv cs.CR 2019
Year: 2019

This article describes experiences and lessons learned from the Trusted CI project, funded by the US National Science Foundation to serve the community as the NSF Cybersecurity Center of Excellence. Trusted CI is an effort to address cybersecurity for the open science community through a single organization that provides leadership, training, consulting, and knowledge to that community. The article describes the experiences and lessons learned of Trusted CI regarding both cybersecurity for open science and managing the process of providing centralized services to a broad and diverse community.
Covariance Evolution for Spatially "Mt. Fuji" Coupled LDPC Codes
Venue: arXiv cs.IT 2019
Year: 2019

A spatially "Mt. Fuji" coupled low-density parity check (LDPC) ensemble is a modified version of the original spatially coupled (SC) LDPC ensemble. Its desirable properties are first observed in experimentally. The decoding error probability in the error floor region over the binary erasure channel (BEC) is theoretically analyzed later. In this paper, as the last piece of the theoretical analysis over the BEC, we analyze the decoding error probability in the waterfall region by modifying the covariance evolution which has been used to analyze the original SC-LDPC ensemble.
Predicting Future Pedestrian Motion in Video Sequences using Crowd Simulation
Venue: arXiv cs.CV 2019
Year: 2019

While human and group analysis have become an important area in last decades, some current and relevant applications involve to estimate future motion of pedestrians in real video sequences. This paper presents a method to provide motion estimation of real pedestrians in next seconds, using crowd simulation. Our method is based on Physics and heuristics and use BioCrowds as crowd simulation methodology to estimate future positions of people in video sequences. Results show that our method for estimation works well even for complex videos where events can happen. The maximum achieved average error is $2.72$cm when estimating the future motion of 32 pedestrians with more than 2 seconds in advance. This paper discusses this and other results.
What Storage Access Privacy is Achievable with Small Overhead?
Venue: arXiv cs.CR 2019
Year: 2019

Oblivious RAM (ORAM) and private information retrieval (PIR) are classic cryptographic primitives used to hide the access pattern to data whose storage has been outsourced to an untrusted server. Unfortunately, both primitives require considerable overhead compared to plaintext access. For large-scale storage infrastructure with highly frequent access requests, the degradation in response time and the exorbitant increase in resource costs incurred by either ORAM or PIR prevent their usage. In an ideal scenario, a privacy-preserving storage protocols with small overhead would be implemented for these heavily trafficked storage systems to avoid negatively impacting either performance and/or costs. In this work, we study the problem of the best $\mathit{storage\ access\ privacy}$ that is achievable with only $\mathit{small\ overhead}$ over plaintext access. To answer this question, we consider $\mathit{differential\ privacy\ access}$ which is a generalization of the $\mathit{oblivious\ access}$ security notion that are considered by ORAM and PIR. Quite surprisingly, we present strong evidence that constant overhead storage schemes may only be achieved with privacy budgets of $ε= Ω(\log n)$. We present asymptotically optimal constructions for differentially private variants of both ORAM and PIR with privacy budgets $ε= Θ(\log n)$ with only $O(1)$ overhead. In addition, we consider a more complex storage primitive called key-value storage in which data is indexed by keys from a large universe (as opposed to consecutive integers in ORAM and PIR). We present a differentially private key-value storage scheme with $ε= Θ(\log n)$ and $O(\log\log n)$ overhead. This construction uses a new oblivious, two-choice hashing scheme that may be of independent interest.
Constant factor approximations to edit distance on far input pairs in nearly linear time
Venue: arXiv cs.DS 2019
Year: 2019

For any $T \geq 1$, there are constants $R=R(T) \geq 1$ and $ζ=ζ(T)>0$ and a randomized algorithm that takes as input an integer $n$ and two strings $x,y$ of length at most $n$, and runs in time $O(n^{1+\frac{1}{T}})$ and outputs an upper bound $U$ on the edit distance $ED(x,y)$ that with high probability, satisfies $U \leq R(ED(x,y)+n^{1-ζ})$. In particular, on any input with $ED(x,y) \geq n^{1-ζ}$ the algorithm outputs a constant factor approximation with high probability. A similar result has been proven independently by Brakensiek and Rubinstein (2019).
Less is More: Real-time Failure Localization in Power Systems
Venue: arXiv cs.SY 2019
Year: 2019

Cascading failures in power systems exhibit non-local propagation patterns which make the analysis and mitigation of failures difficult. In this work, we propose a distributed control framework inspired by the recently proposed concepts of unified controller and network tree-partition that offers strong guarantees in both the mitigation and localization of cascading failures in power systems. In this framework, the transmission network is partitioned into several control areas which are connected in a tree structure, and the unified controller is adopted by generators or controllable loads for fast timescale disturbance response. After an initial failure, the proposed strategy always prevents successive failures from happening, and regulates the system to the desired steady state where the impact of initial failures are localized as much as possible. For extreme failures that cannot be localized, the proposed framework has a configurable design, that progressively involves and coordinates more control areas for failure mitigation and, as a last resort, imposes minimal load shedding. We compare the proposed control framework with Automatic Generation Control (AGC) on the IEEE 118-bus test system. Simulation results show that our novel framework greatly improves the system robustness in terms of the N-1 security standard, and localizes the impact of initial failures in majority of the load profiles that are examined. Moreover, the proposed framework incurs significantly less load loss, if any, compared to AGC, in all of our case studies.
Smart Home Survey on Security and Privacy
Venue: arXiv cs.CR 2019
Year: 2019

Smart homes are a special use-case of the Internet-of-Things (IoT) paradigm. Security and privacy are two prime concern in smart home networks. A threat-prone smart home can reveal lifestyle and behavior of the occupants, which may be a significant concern. This article shows security requirements and threats to a smart home and focuses on a privacy-preserving security model. We classify smart home services based on the spatial and temporal properties of the underlying device-to-device and owner-to-cloud interaction. We present ways to adapt existing security solutions such as distance-bounding protocols, ISO-KE, SIGMA, TLS, Schnorr, Okamoto Identification Scheme (IS), Pedersen commitment scheme for achieving security and privacy in a cloud-assisted home area network.
Predicting Progression of Age-related Macular Degeneration from Fundus Images using Deep Learning
Venue: arXiv cs.CV 2019
Year: 2019

Background: Patients with neovascular age-related macular degeneration (AMD) can avoid vision loss via certain therapy. However, methods to predict the progression to neovascular age-related macular degeneration (nvAMD) are lacking. Purpose: To develop and validate a deep learning (DL) algorithm to predict 1-year progression of eyes with no, early, or intermediate AMD to nvAMD, using color fundus photographs (CFP). Design: Development and validation of a DL algorithm. Methods: We trained a DL algorithm to predict 1-year progression to nvAMD, and used 10-fold cross-validation to evaluate this approach on two groups of eyes in the Age-Related Eye Disease Study (AREDS): none/early/intermediate AMD, and intermediate AMD (iAMD) only. We compared the DL algorithm to the manually graded 4-category and 9-step scales in the AREDS dataset. Main outcome measures: Performance of the DL algorithm was evaluated using the sensitivity at 80% specificity for progression to nvAMD. Results: The DL algorithm's sensitivity for predicting progression to nvAMD from none/early/iAMD (78+/-6%) was higher than manual grades from the 9-step scale (67+/-8%) or the 4-category scale (48+/-3%). For predicting progression specifically from iAMD, the DL algorithm's sensitivity (57+/-6%) was also higher compared to the 9-step grades (36+/-8%) and the 4-category grades (20+/-0%). Conclusions: Our DL algorithm performed better in predicting progression to nvAMD than manual grades. Future investigations are required to test the application of this DL algorithm in a real-world clinical setting.
Synthesizing Database Programs for Schema Refactoring
Venue: arXiv cs.PL 2019
Year: 2019

Many programs that interact with a database need to undergo schema refactoring several times during their life cycle. Since this process typically requires making significant changes to the program's implementation, schema refactoring is often non-trivial and error-prone. Motivated by this problem, we propose a new technique for automatically synthesizing a new version of a database program given its original version and the source and target schemas. Our method does not require manual user guidance and ensures that the synthesized program is equivalent to the original one. Furthermore, our method is quite efficient and can synthesize new versions of database programs (containing up to 263 functions) that are extracted from real-world web applications with an average synthesis time of 69.4 seconds.
Timely-Throughput Optimal Coded Computing over Cloud Networks
Venue: arXiv cs.DC 2019
Year: 2019

In modern distributed computing systems, unpredictable and unreliable infrastructures result in high variability of computing resources. Meanwhile, there is significantly increasing demand for timely and event-driven services with deadline constraints. Motivated by measurements over Amazon EC2 clusters, we consider a two-state Markov model for variability of computing speed in cloud networks. In this model, each worker can be either in a good state or a bad state in terms of the computation speed, and the transition between these states is modeled as a Markov chain which is unknown to the scheduler. We then consider a Coded Computing framework, in which the data is possibly encoded and stored at the worker nodes in order to provide robustness against nodes that may be in a bad state. With timely computation requests submitted to the system with computation deadlines, our goal is to design the optimal computation-load allocation scheme and the optimal data encoding scheme that maximize the timely computation throughput (i.e, the average number of computation tasks that are accomplished before their deadline). Our main result is the development of a dynamic computation strategy called Lagrange Estimate-and Allocate (LEA) strategy, which achieves the optimal timely computation throughput. It is shown that compared to the static allocation strategy, LEA increases the timely computation throughput by 1.4X - 17.5X in various scenarios via simulations and by 1.27X - 6.5X in experiments over Amazon EC2 clusters
Direct Fitting of Gaussian Mixture Models
Venue: arXiv cs.CV 2019
Year: 2019

When fitting Gaussian Mixture Models to 3D geometry, the model is typically fit to point clouds, even when the shapes were obtained as 3D meshes. Here we present a formulation for fitting Gaussian Mixture Models (GMMs) directly to a triangular mesh instead of using points sampled from its surface. Part of this work analyzes a general formulation for evaluating likelihood of geometric objects. This modification enables fitting higher-quality GMMs under a wider range of initialization conditions. Additionally, models obtained from this fitting method are shown to produce an improvement in 3D registration for both meshes and RGB-D frames. This result is general and applicable to arbitrary geometric objects, including representing uncertainty from sensor measurements.
Scalable Cross-Lingual Transfer of Neural Sentence Embeddings
Venue: arXiv cs.CL 2019
Year: 2019

We develop and investigate several cross-lingual alignment approaches for neural sentence embedding models, such as the supervised inference classifier, InferSent, and sequential encoder-decoder models. We evaluate three alignment frameworks applied to these models: joint modeling, representation transfer learning, and sentence mapping, using parallel text to guide the alignment. Our results support representation transfer as a scalable approach for modular cross-lingual alignment of neural sentence embeddings, where we observe better performance compared to joint models in intrinsic and extrinsic evaluations, particularly with smaller sets of parallel data.
Tight Bounds for the Subspace Sketch Problem with Applications
Venue: arXiv cs.DS 2019
Year: 2019

In the subspace sketch problem one is given an $n\times d$ matrix $A$ with $O(\log(nd))$ bit entries, and would like to compress it in an arbitrary way to build a small space data structure $Q_p$, so that for any given $x \in \mathbb{R}^d$, with probability at least $2/3$, one has $Q_p(x)=(1\pmε)\|Ax\|_p$, where $p\geq 0$, and where the randomness is over the construction of $Q_p$. The central question is: How many bits are necessary to store $Q_p$? This problem has applications to the communication of approximating the number of non-zeros in a matrix product, the size of coresets in projective clustering, the memory of streaming algorithms for regression in the row-update model, and embedding subspaces of $L_p$ in functional analysis. A major open question is the dependence on the approximation factor $ε$. We show if $p\geq 0$ is not a positive even integer and $d=Ω(\log(1/ε))$, then $\tildeΩ(ε^{-2}d)$ bits are necessary. On the other hand, if $p$ is a positive even integer, then there is an upper bound of $O(d^p\log(nd))$ bits independent of $ε$. Our results are optimal up to logarithmic factors, and show in particular that one cannot compress $A$ to $O(d)$ "directions" $v_1,\dots,v_{O(d)}$, such that for any $x$, $\|Ax\|_1$ can be well-approximated from $\langle v_1,x\rangle,\dots,\langle v_{O(d)},x\rangle$. Our lower bound rules out arbitrary functions of these inner products (and in fact arbitrary data structures built from $A$), and thus rules out the possibility of a singular value decomposition for $\ell_1$ in a very strong sense. Indeed, as $ε\to 0$, for $p = 1$ the space complexity becomes arbitrarily large, while for $p = 2$ it is at most $O(d^2 \log(nd))$. As corollaries of our main lower bound, we obtain new lower bounds for a wide range of applications, including the above, which in many cases are optimal.
Optimal Dynamic Allocation of Collaborative Servers in Two Station Tandem Systems
Venue: arXiv cs.SY 2019
Year: 2019

We consider two-stage tandem queueing systems with one dedicated server in each station and a flexible server that can serve both stations. We assume exponential service times, linear holding costs accrued by jobs present in the system, and a collaborative work discipline. We seek optimal server allocation strategies for systems without external arrivals (clearing systems). When the combined rate of collaborating servers is less than the sum of their individual rates (partial collaboration), we identify conditions under which the optimal server allocation strategy is non-idling and has a threshold-type structure. Our results extend previous work on systems with additive service rates. When the aforementioned conditions are not satisfied we show by examples that the optimal policy may have counterintuitive properties, which is not the case when a fully collaborative service discipline is assumed. We also obtain novel results for any type of collaboration when idling policies may be optimal.
FRNET: Flattened Residual Network for Infant MRI Skull Stripping
Venue: arXiv cs.CV 2019
Year: 2019

Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull stripping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image without any human assistance. Specifically, we propose a simplified but more robust flattened residual network architecture (FRnet). We also introduce a new boundary loss function to highlight ambiguous and low contrast regions between brain and non-brain regions. To make the whole framework more robust to MR images with different imaging quality, we further introduce an artifact simulator for data augmentation. We have trained and tested our proposed framework on a large dataset (N=343), covering newborns to 48-month-olds, and obtained performance better than the state-of-the-art methods in all age groups.
The α-κ-μ Shadowed Fading Distribution: Statistical Characterization and Applications
Venue: arXiv cs.IT 2019
Year: 2019

We introduce the α-κ-μ shadowed (α-KMS) fading distribution as a natural generalization of the versatile α-κ-μ and α-η-μ distributions. The α-KMS fading distribution unifies a wide set of fading distributions, as it includes the α-κ-μ, α- η-μ, α-μ, Weibull, κ-μ shadowed, Rician shadowed, κ-μ and η- μ distributions as special cases, together with classical models like Rice, Nakagami-m, Hoyt, Rayleigh and one-sided Gaussian. Notably, the α-KMS distribution reduces to a finite mixture of α-μ distributions when the fading parameters μ and m take positive integer values, so that performance analysis over α-KMS fading channels can be tackled by leveraging previous (existing) results in the literature for the simpler α-μ case. As application examples, important performance metrics like the outage probability and average channel capacity are analyzed.
On the sojourn of an arbitrary customer in an $M/M/1$ Processor Sharing Queue
Venue: arXiv cs.PF 2019
Year: 2019

In this paper, we consider the number of both arrivals and departures seen by a tagged customer while in service in a classical $M/M/1$ processor sharing queue. By exploiting the underlying orthogonal structure of this queuing system revealed in an earlier study, we compute the distributions of these two quantities and prove that they are equal in distribution. We moreover derive the asymptotic behavior of this common distribution. The knowledge of the number of departures seen by a tagged customer allows us to test the validity of an approximation, which consists of assuming that the tagged customer is randomly served among those customers in the residual busy period of the queue following the arrival of the tagged customer. A numerical evidence shows that this approximation is reasonable for moderate values of the number of departures, given that the asymptotic behaviors of the distributions are very different even if the exponential decay rates are equal.
Learning to Take Good Pictures of People with a Robot Photographer
Venue: arXiv cs.RO 2019
Year: 2019

We present a robotic system capable of navigating autonomously by following a line and taking good quality pictures of people. When a group of people are detected, the robot rotates towards them and then back to line while continuously taking pictures from different angles. Each picture is processed in the cloud where its quality is estimated in a two-stage algorithm. First, features such as the face orientation and likelihood of facial emotions are input to a fully connected neural network to assign a quality score to each face. Second, a representation is extracted by abstracting faces from the image and it is input to a to Convolutional Neural Network (CNN) to classify the quality of the overall picture. We collected a dataset in which a picture was labeled as good quality if subjects are well-positioned in the image and oriented towards the camera with a pleasant expression. Our approach detected the quality of pictures with 78.4% accuracy in this dataset and received a better mean user rating (3.71/5) than a heuristic method that uses photographic composition procedures in a study where 97 human judges rated each picture. A statistical analysis against the state-of-the-art verified the quality of the resulting pictures.
FTGAN: A Fully-trained Generative Adversarial Networks for Text to Face Generation
Venue: arXiv cs.CV 2019
Year: 2019

As a sub-domain of text-to-image synthesis, text-to-face generation has huge potentials in public safety domain. With lack of dataset, there are almost no related research focusing on text-to-face synthesis. In this paper, we propose a fully-trained Generative Adversarial Network (FTGAN) that trains the text encoder and image decoder at the same time for fine-grained text-to-face generation. With a novel fully-trained generative network, FTGAN can synthesize higher-quality images and urge the outputs of the FTGAN are more relevant to the input sentences. In addition, we build a dataset called SCU-Text2face for text-to-face synthesis. Through extensive experiments, the FTGAN shows its superiority in boosting both generated images' quality and similarity to the input descriptions. The proposed FTGAN outperforms the previous state of the art, boosting the best reported Inception Score to 4.63 on the CUB dataset. On SCU-text2face, the face images generated by our proposed FTGAN just based on the input descriptions is of average 59% similarity to the ground-truth, which set a baseline for text-to-face synthesis.
Malware Evasion Attack and Defense
Venue: arXiv cs.CR 2019
Year: 2019

Machine learning (ML) classifiers are vulnerable to adversarial examples. An adversarial example is an input sample which is slightly modified to induce misclassification in an ML classifier. In this work, we investigate white-box and grey-box evasion attacks to an ML-based malware detector and conduct performance evaluations in a real-world setting. We compare the defense approaches in mitigating the attacks. We propose a framework for deploying grey-box and black-box attacks to malware detection systems.